Setup and Installation¶
Development and documentation occurs on GitHub.
PIDGIN is currently only compatible with Python 2.7.x. but it runs under Anaconda 3 installations as long as the environment is set up with Python 2.7 (see installation instructions below).
It also has the following dependencies:
Required dependencies¶
Install with Conda¶
Follow these steps on Linux/OSX:
- Download and install Anaconda from https://www.continuum.io/downloads
- Open terminal in Mac/Linux and run
conda env create -f pidgin4_env.yml --name pidgin4_env
- N.B. Rdkit may not import on some systems due to a bug. If this happens upgrade to the latest version of conda before creating the above environment using:
conda update conda
- N.B. Installs the IMI eTOX flatkinson standardiser (replaces ChemAxon’s standardizer used in previous PIDGIN versions) and statsmodels for p-value correction in predict_enriched.py
If you encounter an issue (usually occurs when installing the environment on non-Linux systems) try the following:
conda create -c rdkit -c conda-forge --name pidgin4_env python=2.7 rdkit scikit-learn=0.19.0 pydot graphviz standardiser statsmodels
- Now run:
source activate pidgin4_env
(This activates the PIDGINv4 virtual environment. N.B This is required for each new terminal session in order to run PIDGIN in the future) - Navigate the directory you wish to install PIDGINv4 and in Mac/Linux terminal run
git clone https://github.com/BenderGroup/PIDGINv4.git
(recommended) or download/extract the zip from GitHub webpage (not recommended due to inability to pull updates) - Download and unzip the no_ortho_mar22.tar.gz https://doi.org/10.6084/m9.figshare.19108382.v1 (md5sum: dc146e69c8f1638e3741ff7900a97cf3) into the PIDGINv4 main directory to form the no_ortho/ directory (leave all subsequent files compressed)
- N.B Depending on bandwidth, Step 5 may take some time
NOTE: For older models and orthologue models, please contact us.
[1] | Mervin, L H., et al. Orthologue chemical space and its influence on target prediction. Bioinformatics. 34: 72–79 (2018) |